Medical imaging techniques are used in many medical procedures, including, for example, in the detection of cancer or precancerous conditions in a patient. An important application is in the detection of tumors or potential tumors in breast cancer. Potential tumors are difficult to detect. Among available techniques providing potentially helpful information, it is known, for example, that such tumor-related tissue may typically exhibit a more rapid intake (wash-in) of contrast agent, as well as a more rapid washout than adjacent, non-tumor tissue. Characteristics such as these and others, may be helpful in certain diagnoses involving detection of suspect tissue and identifying tissue characteristics through a comparison of images of a patient made before and after a procedure, such as wash-in and/or washout of contrast agent. Using such time sequential images made by an imaging technique such as magnetic resonance imaging (MRI), a comparison may be made between images to detect differences in behavior exhibited by different regions of the acquired MR volume.
A technique for performing this detection advantageously requires one to track the intensity of a single voxel in a temporal sequence of such volumes. However, a difficulty arises in that the patient typically moves between consecutive acquisitions and thereby introduces motion-related differences between the acquired images whereby a single point in space can no longer be tracked, unless motion correction is performed. As used herein, a point in space is not intended to mean a classical geometrically defined point of no dimension but rather a point resulting from a digitization procedure having the small dimensions of elements which go to make up a digitized image.
Prior art approaches to solving this problem in the past have computed the optic-flow between two images, of which an arbitrary one is selected as reference among the images of the sequence. For example, the two images can be obtained from the acquired images by computing a Laplacian pyramid. The optic flow may, for example, be calculated by solving a minimization problem of the point-to-point difference between the two Laplacian images.